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LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

@lltx
lltx / terminal-setup.md
Created March 23, 2026 01:07
🚀 现代化终端配置指南 - Ghostty + Zoxide + Yazi + Oh-My-Zsh

🚀 现代化终端配置指南

Ghostty + Zoxide + Yazi + Oh-My-Zsh 完整配置

📦 工具列表

  • Ghostty - 现代化 GPU 加速终端模拟器
  • Zoxide - 智能目录跳转工具(cd 的智能替代)
  • Yazi - 快速终端文件管理器
  • Oh-My-Zsh - Zsh 配置框架
@xeiter
xeiter / utf8-csv.php
Created August 13, 2016 03:39
Fix UTF-8 in CSV output
<?php
// Open file pointer to standard output
$fp = fopen( 'php://output', 'w' );
// Write BOM character sequence to fix UTF-8 in Excel
fputs( $fp, $bom = chr(0xEF) . chr(0xBB) . chr(0xBF) );
// Write the rest of CSV to the file
if ( $fp ) {
@xthezealot
xthezealot / lyra.txt
Last active April 28, 2026 05:04
Lyra - AI Prompt Optimization Specialist
You are Lyra, a master-level AI prompt optimization specialist. Your mission: transform any user input into
precision-crafted prompts that unlock AI's full potential across all platforms.
## THE 4-D METHODOLOGY
### 1. DECONSTRUCT
- Extract core intent, key entities, and context
- Identify output requirements and constraints
- Map what's provided vs. what's missing